
import numpy as np
import pandas as pd
import plotly.express as px
from sklearn.cluster import KMeans
import seaborn as sns
import matplotlib.pyplot as plt
import re
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix,accuracy_score,classification_report
from googletrans import Translator
from sklearn.cluster import KMeans
import warnings
warnings.filterwarnings('ignore')


df = pd.read_csv('Amazon Sale Report.csv')
df.T


def sniff_modified(df):
    with pd.option_context("display.max_colwidth", 20):
        info = pd.DataFrame()
        info['data type'] = df.dtypes
        info['percent missing'] = df.isnull().sum()*100/len(df)
        info['No. unique'] = df.apply(lambda x: len(x.unique()))
        info['unique values'] = df.apply(lambda x: x.unique())
        return info.sort_values('data type')


sniff_modified(df)


df['Date']=pd.to_datetime(df['Date'])


df.head().T


df.isnull().sum()

index                     0
Order ID                  0
Date                      0
Status                    0
Fulfilment                0
Sales Channel             0
ship-service-level        0
Style                     0
SKU                       0
Category                  0
Size                      0
ASIN                      0
Courier Status         6872
Qty                       0
currency               7795
Amount                 7795
ship-city                33
ship-state               33
ship-postal-code         33
ship-country             33
promotion-ids         49153
B2B                       0
fulfilled-by          89698
Unnamed: 22           49050
dtype: int64


columns_to_delete = ['Unnamed: 22', 'fulfilled-by', 'promotion-ids', 'ship-postal-code', 'currency', 'ship-country','Sales Channel ', 'index', 'SKU']
df = df.drop(columns=columns_to_delete)


sniff_modified(df)


df.shape

(128975, 15)


df_cleaned_rows = df.dropna()
sniff_modified(df_cleaned_rows)


df_cleaned_rows.shape

(116016, 15)


## !pip install googletrans==4.0.0-rc1


translator = Translator()

def translate_to_english(text):
    if text.isascii():
        return text
    else:
        translation = translator.translate(text, src='hi', dest='en')
        return translation.text

df_cleaned_rows['ship-city'] = df_cleaned_rows['ship-city'].apply(translate_to_english)


df_cleaned_rows['ship-city']=df_cleaned_rows['ship-city'].str.capitalize()


df_cleaned_rows['ship-city']=df_cleaned_rows['ship-city'].replace({'Guwahati, kamrup (m)': 'Guwahati',
                                                                   'Kolkata 700034':'Kolkata',
                                                                   'Dhamnod dhar district': 'Dhamnod dhar',
                                                                   'Nk sweets vikasnagar':'vikasnagar',
                                                                   'Mandvi kachchh district':'Mandvi kachchh',
                                                                   'Andul near maya stores':'Andul',
                                                                   'district': '',
                                                                   'Neelakudi, thiruvarur': 'Neelakudi',
                                                                   'Arvi,dist- wardha':'Arvi',
                                                                   'Mumbai 400101': 'Mumbai',
                                                                   'Kamatgi,hunugund taluk,bagalkot district':'bagalkot',
                                                                   'extension': '',
                                                                   'Durganagar ,nimta south':'Durganagar',
                                                                   'and  ':'',
                                                                   'Mumbai,malad west,malvani.':'malvani',
                                                                   'Hassan (amazon arun)':'Hassan',
                                                                   'Phanigiri road,chaitanyapuri,hyderabad':'chaitanyapuri',
                                                                   'Town.budhana village. husainpur kalan':'husainpur',
                                                                   'New delhi-110075':'New delhi',
                                                                   'Mumbai -400064':'Mumbai',
                                                                   'Mumbai 400023':'Mumbai',
                                                                   'Post office-harrawala, dehradun':'dehradun',
                                                                   'Nuvem.   ( do not ring the door bell, call b4 comg':'Nuvem',
                                                                   'Tehsil - sikandrabad, district - bulandshahr':'bulandshahr',
                                                                   'Mumbai 400057':'Mumbai',
                                                                   'Pune-412207':'Pune'
                                                                    })


df_cleaned_rows['ship-city'].unique()

array(['Bengaluru', 'Navi mumbai', 'Chennai', ...,
       'Vaishali nagar nagpur', 'Gulabpura, distt bhilwara',
       'Prayagraj (allahabad)'], dtype=object)


df_cleaned_rows['ship-state']=df_cleaned_rows['ship-state'].str.capitalize()


df_cleaned_rows['ship-state']=df_cleaned_rows['ship-state'].replace({'Punjab/mohali/zirakpur': 'Punjab',
                                                                     'rajshthan': 'Rajasthan',
                                                                    'Puducherry':'Pondicherry',
                                                                    'Pb': 'Punjab',
                                                                    'Ar': 'Arunachal pradesh',
                                                                    'Nl': 'Nagaland',
                                                                    'Rj': 'Rajasthan',
                                                                    'Orissa':'Odisha',
                                                                     'Rajsthan': 'Rajasthan',
                                                                     'New delhi': 'Delhi'
                                                                    })


df_cleaned_rows['ship-state'].unique()

array(['Karnataka', 'Maharashtra', 'Tamil nadu', 'Uttar pradesh',
       'Chandigarh', 'Telangana', 'Andhra pradesh', 'Rajasthan', 'Delhi',
       'Haryana', 'Assam', 'Jharkhand', 'Chhattisgarh', 'Odisha',
       'Kerala', 'Madhya pradesh', 'West bengal', 'Nagaland', 'Gujarat',
       'Uttarakhand', 'Bihar', 'Jammu & kashmir', 'Punjab',
       'Himachal pradesh', 'Arunachal pradesh', 'Manipur', 'Goa',
       'Meghalaya', 'Pondicherry', 'Tripura', 'Ladakh', 'Dadra and nagar',
       'Sikkim', 'Andaman & nicobar ', 'Rajshthan', 'Mizoram',
       'Lakshadweep'], dtype=object)


df_cleaned_rows['Order ID'] = df_cleaned_rows['Order ID'].apply(lambda x: re.sub(r'\D', '', str(x)))

df_cleaned_rows['Order ID'] = pd.to_numeric(df_cleaned_rows['Order ID'], errors='coerce')


df_cleaned_rows.shape

(116016, 15)


# df_cleaned_rows.to_excel('marketing.xlsx')


top5_size = df_cleaned_rows.groupby('Size')['Qty'].sum().sort_values(ascending = False).head(5)
top5_size

Size
M      20422
L      19963
XL     18898
XXL    16487
S      15309
Name: Qty, dtype: int64


top5_city = df_cleaned_rows.groupby('ship-state')['Amount'].sum().sort_values(ascending = False).head(5)
top5_city

ship-state
Maharashtra      12864511.0
Karnataka        10153100.0
Telangana         6642955.0
Uttar pradesh     6494393.0
Tamil nadu        6241913.0
Name: Amount, dtype: float64


# df_cleaned_rows.to_csv('output1.csv', index=False)


categorical_cols= ['B2B','Fulfilment','ship-service-level','Courier Status', 'Size', 'Status', 'Category', 'Qty', 'Style','ASIN','ship-city','ship-state' ]


categorical_cols

['B2B',
 'Fulfilment',
 'ship-service-level',
 'Courier Status',
 'Size',
 'Status',
 'Category',
 'Qty',
 'Style',
 'ASIN',
 'ship-city',
 'ship-state']


all_cols = ['Order ID','Amount']


all_cols

['Order ID', 'Amount']


for col in all_cols:
    fig, ax = plt.subplots(figsize=(10, 6))
    df_cleaned_rows[col].hist(bins=10)
    plt.title(f'Histogram of {col}')
    plt.xlabel(col)
    plt.ylabel('Frequency')
    plt.show()
    print(df_cleaned_rows[col].mean())
    print(df_cleaned_rows[col].median())

3.768420902031196e+16
4.050856516269754e+16

649.7808491932147
606.0


for col in categorical_cols:
    plt.figure(figsize=(15, 6))
    
    # Plot only the top 10 categories for better readability
    top_n = 20
    df_cleaned_rows[col].value_counts().nlargest(top_n).plot(kind='bar', color='skyblue')
    
    plt.title(f'Bar Plot of {col}')
    plt.xlabel(col)
    plt.ylabel('Frequency')
    plt.xticks(rotation=90)
    plt.show()

    dominating_category = df_cleaned_rows[col].value_counts().idxmax()
    print(f"In column '{col}', the dominating category is '{dominating_category}' with {df_cleaned_rows[col].value_counts().max()} occurrences.\n")

In column 'B2B', the dominating category is 'False' with 115191 occurrences.

In column 'Fulfilment', the dominating category is 'Amazon' with 83621 occurrences.

In column 'ship-service-level', the dominating category is 'Expedited' with 82705 occurrences.

In column 'Courier Status', the dominating category is 'Shipped' with 109461 occurrences.

In column 'Size', the dominating category is 'M' with 20339 occurrences.

In column 'Status', the dominating category is 'Shipped' with 77580 occurrences.

In column 'Category', the dominating category is 'Set' with 45079 occurrences.

In column 'Qty', the dominating category is '1' with 115637 occurrences.

In column 'Style', the dominating category is 'JNE3797' with 3676 occurrences.

In column 'ASIN', the dominating category is 'B09SDXFFQ1' with 660 occurrences.


df_cleaned_rows['RecencyDate'] = df_cleaned_rows['Date']


current_date = df_cleaned_rows['Date'].max()

rfm_data = df_cleaned_rows.groupby('ship-state').agg({
    'RecencyDate': lambda x: (current_date - x.max()).days,  # Recency
    'Date': 'count',                                   # Frequency
    'Amount': 'sum'                                      # Monetary
}).rename(columns={
    'RecencyDate': 'Recency',
    'Date': 'Frequency',
    'Amount': 'Monetary'
})

rfm_data


scaler = StandardScaler()
normalized_rfm = pd.DataFrame(scaler.fit_transform(rfm_data), columns=rfm_data.columns)
print(normalized_rfm)

     Recency  Frequency  Monetary
0  -0.238096  -0.631746 -0.642679
1  -0.238096   0.359937  0.343783
2  -0.142340  -0.650833 -0.660086
3  -0.238096  -0.358664 -0.360353
4  -0.238096  -0.274940 -0.233071
5  -0.238096  -0.614177 -0.623222
6  -0.238096  -0.502255 -0.508214
7   0.049172  -0.667535 -0.678976
8  -0.238096   0.704813  0.745406
9  -0.238096  -0.452150 -0.480891
10 -0.238096   0.181208  0.197585
11 -0.238096   0.196825  0.256215
12 -0.142340  -0.533706 -0.532429
13 -0.238096  -0.546937 -0.545286
14 -0.238096  -0.402696 -0.397050
15 -0.238096   2.756503  2.757714
16 -0.238096   0.576406  0.537251
17 -0.142340  -0.671439 -0.679838
18  1.964292  -0.679465 -0.691492
19 -0.238096  -0.190347 -0.173942
20 -0.238096   3.717168  3.679054
21 -0.142340  -0.616346 -0.620650
22 -0.238096  -0.640205 -0.654524
23 -0.142340  -0.665583 -0.679238
24 -0.238096  -0.642808 -0.644706
25 -0.238096  -0.267131 -0.242143
26 -0.238096  -0.613309 -0.630411
27 -0.238096  -0.304439 -0.296890
28 -0.238096  -0.154341 -0.120709
29  5.603020  -0.679682 -0.691939
30 -0.238096  -0.640639 -0.646501
31 -0.238096   1.563751  1.428688
32 -0.238096   1.534035  1.564962
33 -0.238096  -0.651050 -0.662827
34 -0.238096   1.372225  1.514481
35 -0.238096  -0.380138 -0.375520
36 -0.238096   0.469690  0.448449


rfm_data


wcss = []
for i in range(1, 7):
    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)
    kmeans.fit(normalized_rfm)
    wcss.append(kmeans.inertia_)

# Plot the Elbow method
plt.figure(figsize=(10,5))
plt.plot(range(1, 7), wcss, marker='o', linestyle='--')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.title('K-means clustering Elbow Method')
plt.show()



optimal_clusters = 2 
kmeans = KMeans(n_clusters=optimal_clusters, init='k-means++', random_state=42)
clusters = kmeans.fit_predict(normalized_rfm)

rfm_data['Cluster'] = clusters


kmeans = KMeans(n_clusters = 2)
y_kmeans = kmeans.fit_predict(normalized_rfm)

rfm_data['Cluster_ID'] = y_kmeans

rfm_data


facet = sns.lmplot(rfm_data, x='Frequency', y='Monetary', hue='Cluster_ID', 
                   fit_reg=False, legend=True, legend_out=True)


import plotly.express as px

fig = px.scatter_3d(rfm_data, x='Frequency', y='Monetary', z='Recency', color='Cluster_ID', hover_name = rfm_data.index )
fig.show()


rfm_data[rfm_data['Cluster_ID'] == 1]


label_encoder = LabelEncoder()
df_cleaned_rows[categorical_cols]=df_cleaned_rows[categorical_cols].apply(LabelEncoder().fit_transform)


min_max_scaler = MinMaxScaler()
df_cleaned_rows[all_cols] = min_max_scaler.fit_transform(df_cleaned_rows[all_cols])


df_cleaned_rows


id_col= ['Order ID']
drop_col= ['Courier Status','B2B','Date','RecencyDate']
target_col = ['Status']


X = df_cleaned_rows.drop(target_col, axis=1)
X = df_cleaned_rows.drop(drop_col, axis=1)
y=df_cleaned_rows[target_col]


X.shape, y.shape

((116016, 12), (116016, 1))

X


X_train, X_test, y_train, y_test = train_test_split(X,y, test_size= 0.20)


lr = LogisticRegression()
lr.fit(X_train,y_train)
y_pred = lr.predict(X_test)


accuracy = accuracy_score(y_test, y_pred)
classification_rep = classification_report(y_test, y_pred)
print(f'Accuracy: {accuracy:.4f}')
print('\nClassification Report:')
print(classification_rep)

Accuracy: 0.6757

Classification Report:
              precision    recall  f1-score   support

           0       0.00      0.00      0.00      1095
           1       0.00      0.00      0.00       153
           2       0.00      0.00      0.00        60
           3       0.68      1.00      0.81     15679
           4       0.00      0.00      0.00         1
           5       0.00      0.00      0.00      5566
           7       0.00      0.00      0.00         5
           8       0.00      0.00      0.00       205
           9       0.00      0.00      0.00         5
          10       0.00      0.00      0.00       405
          11       0.00      0.00      0.00        30

    accuracy                           0.68     23204
   macro avg       0.06      0.09      0.07     23204
weighted avg       0.46      0.68      0.54     23204


clf = RandomForestClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)


accuracy = accuracy_score(y_test, y_pred)
classification_rep = classification_report(y_test, y_pred)
print(f'Accuracy: {accuracy:.4f}')
print('\nClassification Report:')
print(classification_rep)

Accuracy: 0.9994

Classification Report:
              precision    recall  f1-score   support

           0       1.00      1.00      1.00      1095
           1       1.00      1.00      1.00       153
           2       1.00      1.00      1.00        60
           3       1.00      1.00      1.00     15679
           4       0.00      0.00      0.00         1
           5       1.00      1.00      1.00      5566
           7       1.00      0.40      0.57         5
           8       0.96      1.00      0.98       205
           9       0.00      0.00      0.00         5
          10       0.99      1.00      0.99       405
          11       1.00      0.80      0.89        30

    accuracy                           1.00     23204
   macro avg       0.81      0.75      0.77     23204
weighted avg       1.00      1.00      1.00     23204

	0	1	2	3	4	5	6	7	8	9	...	128965	128966	128967	128968	128969	128970	128971	128972	128973	128974
index	0	1	2	3	4	5	6	7	8	9	...	128965	128966	128967	128968	128969	128970	128971	128972	128973	128974
Order ID	405-8078784-5731545	171-9198151-1101146	404-0687676-7273146	403-9615377-8133951	407-1069790-7240320	404-1490984-4578765	408-5748499-6859555	406-7807733-3785945	407-5443024-5233168	402-4393761-0311520	...	408-5154281-4593912	406-9812666-2474761	404-5182288-1653947	403-7059995-7618722	404-3802633-7250760	406-6001380-7673107	402-9551604-7544318	407-9547469-3152358	402-6184140-0545956	408-7436540-8728312
Date	04-30-22	04-30-22	04-30-22	04-30-22	04-30-22	04-30-22	04-30-22	04-30-22	04-30-22	04-30-22	...	05-31-22	05-31-22	05-31-22	05-31-22	05-31-22	05-31-22	05-31-22	05-31-22	05-31-22	05-31-22
Status	Cancelled	Shipped - Delivered to Buyer	Shipped	Cancelled	Shipped	Shipped	Shipped	Shipped - Delivered to Buyer	Cancelled	Shipped	...	Cancelled	Shipped	Cancelled	Shipped	Cancelled	Shipped	Shipped	Shipped	Shipped	Shipped
Fulfilment	Merchant	Merchant	Amazon	Merchant	Amazon	Amazon	Amazon	Merchant	Amazon	Amazon	...	Amazon	Amazon	Amazon	Amazon	Amazon	Amazon	Amazon	Amazon	Amazon	Amazon
Sales Channel	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	...	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in
ship-service-level	Standard	Standard	Expedited	Standard	Expedited	Expedited	Expedited	Standard	Expedited	Expedited	...	Expedited	Expedited	Expedited	Expedited	Expedited	Expedited	Expedited	Expedited	Expedited	Expedited
Style	SET389	JNE3781	JNE3371	J0341	JNE3671	SET264	J0095	JNE3405	SET200	JNE3461	...	J0119	SET224	JNE3638	SET264	SET044	JNE3697	SET401	J0157	J0012	J0003
SKU	SET389-KR-NP-S	JNE3781-KR-XXXL	JNE3371-KR-XL	J0341-DR-L	JNE3671-TU-XXXL	SET264-KR-NP-XL	J0095-SET-L	JNE3405-KR-S	SET200-KR-NP-A-XXXL	JNE3461-KR-XXL	...	J0119-TP-XXXL	SET224-KR-NP-XS	JNE3638-KR-XS	SET264-KR-NP-XL	SET044-KR-NP-M	JNE3697-KR-XL	SET401-KR-NP-M	J0157-DR-XXL	J0012-SKD-XS	J0003-SET-S
Category	Set	kurta	kurta	Western Dress	Top	Set	Set	kurta	Set	kurta	...	Top	Set	kurta	Set	Set	kurta	Set	Western Dress	Set	Set
Size	S	3XL	XL	L	3XL	XL	L	S	3XL	XXL	...	3XL	XS	XS	XL	M	XL	M	XXL	XS	S
ASIN	B09KXVBD7Z	B09K3WFS32	B07WV4JV4D	B099NRCT7B	B098714BZP	B08YN7XDSG	B08CMHNWBN	B081WX4G4Q	B08L91ZZXN	B08B3XF5MH	...	B08RYPRVPV	B08MXDBRK1	B09814Q3QH	B08YN7XDSG	B07Q2RTSFB	B098112V2V	B09VC6KHX8	B0982YZ51B	B0894Y2NJQ	B0894X27FC
Courier Status	NaN	Shipped	Shipped	NaN	Shipped	Shipped	Shipped	Shipped	Cancelled	Shipped	...	Unshipped	Shipped	Cancelled	Shipped	Unshipped	Shipped	Shipped	Shipped	Shipped	Shipped
Qty	0	1	1	0	1	1	1	1	0	1	...	1	1	0	1	1	1	1	1	1	1
currency	INR	INR	INR	INR	INR	INR	INR	INR	NaN	INR	...	INR	INR	NaN	INR	INR	INR	INR	INR	INR	INR
Amount	647.62	406.0	329.0	753.33	574.0	824.0	653.0	399.0	NaN	363.0	...	574.0	1132.0	NaN	824.0	612.0	517.0	999.0	690.0	1199.0	696.0
ship-city	MUMBAI	BENGALURU	NAVI MUMBAI	PUDUCHERRY	CHENNAI	GHAZIABAD	CHANDIGARH	HYDERABAD	HYDERABAD	Chennai	...	Prayagraj (ALLAHABAD)	CHENNAI 600042	Kolkata	Delhi	MUMBAI	HYDERABAD	GURUGRAM	HYDERABAD	Halol	Raipur
ship-state	MAHARASHTRA	KARNATAKA	MAHARASHTRA	PUDUCHERRY	TAMIL NADU	UTTAR PRADESH	CHANDIGARH	TELANGANA	TELANGANA	TAMIL NADU	...	UTTAR PRADESH	TAMIL NADU	WEST BENGAL	DELHI	MAHARASHTRA	TELANGANA	HARYANA	TELANGANA	Gujarat	CHHATTISGARH
ship-postal-code	400081.0	560085.0	410210.0	605008.0	600073.0	201102.0	160036.0	500032.0	500008.0	600041.0	...	211007.0	600042.0	700040.0	110053.0	400017.0	500013.0	122004.0	500049.0	389350.0	492014.0
ship-country	IN	IN	IN	IN	IN	IN	IN	IN	IN	IN	...	IN	IN	IN	IN	IN	IN	IN	IN	IN	IN
promotion-ids	NaN	Amazon PLCC Free-Financing Universal Merchant ...	IN Core Free Shipping 2015/04/08 23-48-5-108	NaN	NaN	IN Core Free Shipping 2015/04/08 23-48-5-108	IN Core Free Shipping 2015/04/08 23-48-5-108	Amazon PLCC Free-Financing Universal Merchant ...	IN Core Free Shipping 2015/04/08 23-48-5-108	NaN	...	NaN	NaN	NaN	IN Core Free Shipping 2015/04/08 23-48-5-108	NaN	NaN	IN Core Free Shipping 2015/04/08 23-48-5-108	NaN	IN Core Free Shipping 2015/04/08 23-48-5-108	IN Core Free Shipping 2015/04/08 23-48-5-108
B2B	False	False	True	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
fulfilled-by	Easy Ship	Easy Ship	NaN	Easy Ship	NaN	NaN	NaN	Easy Ship	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Unnamed: 22	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	False	False	False	False	False	False	False	False	False	False

	0	1	2	3	4
index	0	1	2	3	4
Order ID	405-8078784-5731545	171-9198151-1101146	404-0687676-7273146	403-9615377-8133951	407-1069790-7240320
Date	2022-04-30 00:00:00	2022-04-30 00:00:00	2022-04-30 00:00:00	2022-04-30 00:00:00	2022-04-30 00:00:00
Status	Cancelled	Shipped - Delivered to Buyer	Shipped	Cancelled	Shipped
Fulfilment	Merchant	Merchant	Amazon	Merchant	Amazon
Sales Channel	Amazon.in	Amazon.in	Amazon.in	Amazon.in	Amazon.in
ship-service-level	Standard	Standard	Expedited	Standard	Expedited
Style	SET389	JNE3781	JNE3371	J0341	JNE3671
SKU	SET389-KR-NP-S	JNE3781-KR-XXXL	JNE3371-KR-XL	J0341-DR-L	JNE3671-TU-XXXL
Category	Set	kurta	kurta	Western Dress	Top
Size	S	3XL	XL	L	3XL
ASIN	B09KXVBD7Z	B09K3WFS32	B07WV4JV4D	B099NRCT7B	B098714BZP
Courier Status	NaN	Shipped	Shipped	NaN	Shipped
Qty	0	1	1	0	1
currency	INR	INR	INR	INR	INR
Amount	647.62	406.0	329.0	753.33	574.0
ship-city	MUMBAI	BENGALURU	NAVI MUMBAI	PUDUCHERRY	CHENNAI
ship-state	MAHARASHTRA	KARNATAKA	MAHARASHTRA	PUDUCHERRY	TAMIL NADU
ship-postal-code	400081.0	560085.0	410210.0	605008.0	600073.0
ship-country	IN	IN	IN	IN	IN
promotion-ids	NaN	Amazon PLCC Free-Financing Universal Merchant ...	IN Core Free Shipping 2015/04/08 23-48-5-108	NaN	NaN
B2B	False	False	True	False	False
fulfilled-by	Easy Ship	Easy Ship	NaN	Easy Ship	NaN
Unnamed: 22	NaN	NaN	NaN	NaN	NaN

	Recency	Frequency	Monetary
ship-state
Andaman & nicobar	0	223	146093.0
Andhra pradesh	0	4795	3049151.0
Arunachal pradesh	1	135	94865.0
Assam	0	1482	976951.0
Bihar	0	1868	1351529.0
Chandigarh	0	304	203354.0
Chhattisgarh	0	820	541811.0
Dadra and nagar	3	58	39276.0
Delhi	0	6385	4231084.0
Goa	0	1051	622220.0
Gujarat	0	3971	2618903.0
Haryana	0	4043	2791446.0
Himachal pradesh	1	675	470548.0
Jammu & kashmir	0	614	432712.0
Jharkhand	0	1279	868953.0
Karnataka	0	15844	10153100.0
Kerala	0	5793	3618505.0
Ladakh	1	40	36737.0
Lakshadweep	23	3	2441.0
Madhya pradesh	0	2258	1525537.0
Maharashtra	0	20273	12864511.0
Manipur	1	294	210922.0
Meghalaya	0	184	111234.0
Mizoram	1	67	38503.0
Nagaland	0	172	140129.0
Odisha	0	1904	1324830.0
Pondicherry	0	308	182198.0
Punjab	0	1732	1163716.0
Rajasthan	0	2424	1682199.0
Rajshthan	61	2	1126.0
Sikkim	0	182	134847.0
Tamil nadu	0	10345	6241913.0
Telangana	0	10208	6642955.0
Tripura	0	134	86799.0
Uttar pradesh	0	9462	6494393.0
Uttarakhand	0	1383	932314.0
West bengal	0	5301	3357170.0

	Recency	Frequency	Monetary
ship-state
Andaman & nicobar	0	223	146093.0
Andhra pradesh	0	4795	3049151.0
Arunachal pradesh	1	135	94865.0
Assam	0	1482	976951.0
Bihar	0	1868	1351529.0
Chandigarh	0	304	203354.0
Chhattisgarh	0	820	541811.0
Dadra and nagar	3	58	39276.0
Delhi	0	6385	4231084.0
Goa	0	1051	622220.0
Gujarat	0	3971	2618903.0
Haryana	0	4043	2791446.0
Himachal pradesh	1	675	470548.0
Jammu & kashmir	0	614	432712.0
Jharkhand	0	1279	868953.0
Karnataka	0	15844	10153100.0
Kerala	0	5793	3618505.0
Ladakh	1	40	36737.0
Lakshadweep	23	3	2441.0
Madhya pradesh	0	2258	1525537.0
Maharashtra	0	20273	12864511.0
Manipur	1	294	210922.0
Meghalaya	0	184	111234.0
Mizoram	1	67	38503.0
Nagaland	0	172	140129.0
Odisha	0	1904	1324830.0
Pondicherry	0	308	182198.0
Punjab	0	1732	1163716.0
Rajasthan	0	2424	1682199.0
Rajshthan	61	2	1126.0
Sikkim	0	182	134847.0
Tamil nadu	0	10345	6241913.0
Telangana	0	10208	6642955.0
Tripura	0	134	86799.0
Uttar pradesh	0	9462	6494393.0
Uttarakhand	0	1383	932314.0
West bengal	0	5301	3357170.0

	Recency	Frequency	Monetary	Cluster	Cluster_ID
ship-state
Andaman & nicobar	0	223	146093.0	0	1
Andhra pradesh	0	4795	3049151.0	0	1
Arunachal pradesh	1	135	94865.0	0	1
Assam	0	1482	976951.0	0	1
Bihar	0	1868	1351529.0	0	1
Chandigarh	0	304	203354.0	0	1
Chhattisgarh	0	820	541811.0	0	1
Dadra and nagar	3	58	39276.0	0	1
Delhi	0	6385	4231084.0	0	1
Goa	0	1051	622220.0	0	1
Gujarat	0	3971	2618903.0	0	1
Haryana	0	4043	2791446.0	0	1
Himachal pradesh	1	675	470548.0	0	1
Jammu & kashmir	0	614	432712.0	0	1
Jharkhand	0	1279	868953.0	0	1
Karnataka	0	15844	10153100.0	1	0
Kerala	0	5793	3618505.0	0	1
Ladakh	1	40	36737.0	0	1
Lakshadweep	23	3	2441.0	0	1
Madhya pradesh	0	2258	1525537.0	0	1
Maharashtra	0	20273	12864511.0	1	0
Manipur	1	294	210922.0	0	1
Meghalaya	0	184	111234.0	0	1
Mizoram	1	67	38503.0	0	1
Nagaland	0	172	140129.0	0	1
Odisha	0	1904	1324830.0	0	1
Pondicherry	0	308	182198.0	0	1
Punjab	0	1732	1163716.0	0	1
Rajasthan	0	2424	1682199.0	0	1
Rajshthan	61	2	1126.0	0	1
Sikkim	0	182	134847.0	0	1
Tamil nadu	0	10345	6241913.0	1	0
Telangana	0	10208	6642955.0	1	0
Tripura	0	134	86799.0	0	1
Uttar pradesh	0	9462	6494393.0	1	0
Uttarakhand	0	1383	932314.0	0	1
West bengal	0	5301	3357170.0	0	1

Top 5 products (using Size) sold by Qty¶

Top 5 state (using ship-state) sold by Amount¶

RFM Analysis by Ship-State¶

Use KMeans clustering to segment customers into different groups.¶

Advanced Machine Learning Analysis¶

Feature Engineering¶

Logistic Regression¶

Random Forest¶

	data type	percent missing	No. unique	unique values
B2B	bool	0.000000	2	[False, True]
index	int64	0.000000	128975	[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...
Qty	int64	0.000000	10	[0, 1, 2, 15, 3, 9, 13, 5, 4, 8]
ship-postal-code	float64	0.025586	9460	[400081.0, 560085.0, 410210.0, 605008.0, 60007...
Amount	float64	6.043807	1411	[647.62, 406.0, 329.0, 753.33, 574.0, 824.0, 6...
promotion-ids	object	38.110487	5788	[nan, Amazon PLCC Free-Financing Universal Mer...
ship-country	object	0.025586	2	[IN, nan]
ship-state	object	0.025586	70	[MAHARASHTRA, KARNATAKA, PUDUCHERRY, TAMIL NAD...
ship-city	object	0.025586	8956	[MUMBAI, BENGALURU, NAVI MUMBAI, PUDUCHERRY, C...
currency	object	6.043807	2	[INR, nan]
Courier Status	object	5.328164	4	[nan, Shipped, Cancelled, Unshipped]
ASIN	object	0.000000	7190	[B09KXVBD7Z, B09K3WFS32, B07WV4JV4D, B099NRCT7...
Size	object	0.000000	11	[S, 3XL, XL, L, XXL, XS, 6XL, M, 4XL, 5XL, Free]
Category	object	0.000000	9	[Set, kurta, Western Dress, Top, Ethnic Dress,...
SKU	object	0.000000	7195	[SET389-KR-NP-S, JNE3781-KR-XXXL, JNE3371-KR-X...
Style	object	0.000000	1377	[SET389, JNE3781, JNE3371, J0341, JNE3671, SET...
ship-service-level	object	0.000000	2	[Standard, Expedited]
Sales Channel	object	0.000000	2	[Amazon.in, Non-Amazon]
Fulfilment	object	0.000000	2	[Merchant, Amazon]
Status	object	0.000000	13	[Cancelled, Shipped - Delivered to Buyer, Ship...
Date	object	0.000000	91	[04-30-22, 04-29-22, 04-28-22, 04-27-22, 04-26...
Order ID	object	0.000000	120378	[405-8078784-5731545, 171-9198151-1101146, 404...
fulfilled-by	object	69.546811	2	[Easy Ship, nan]
Unnamed: 22	object	38.030626	2	[nan, False]

	data type	percent missing	No. unique	unique values
Date	datetime64[ns]	0.000000	91	[2022-04-30 00:00:00, 2022-04-29 00:00:00, 202...
B2B	bool	0.000000	2	[False, True]
Qty	int64	0.000000	10	[0, 1, 2, 15, 3, 9, 13, 5, 4, 8]
Amount	float64	6.043807	1411	[647.62, 406.0, 329.0, 753.33, 574.0, 824.0, 6...
Order ID	object	0.000000	120378	[405-8078784-5731545, 171-9198151-1101146, 404...
Status	object	0.000000	13	[Cancelled, Shipped - Delivered to Buyer, Ship...
Fulfilment	object	0.000000	2	[Merchant, Amazon]
ship-service-level	object	0.000000	2	[Standard, Expedited]
Style	object	0.000000	1377	[SET389, JNE3781, JNE3371, J0341, JNE3671, SET...
Category	object	0.000000	9	[Set, kurta, Western Dress, Top, Ethnic Dress,...
Size	object	0.000000	11	[S, 3XL, XL, L, XXL, XS, 6XL, M, 4XL, 5XL, Free]
ASIN	object	0.000000	7190	[B09KXVBD7Z, B09K3WFS32, B07WV4JV4D, B099NRCT7...
Courier Status	object	5.328164	4	[nan, Shipped, Cancelled, Unshipped]
ship-city	object	0.025586	8956	[MUMBAI, BENGALURU, NAVI MUMBAI, PUDUCHERRY, C...
ship-state	object	0.025586	70	[MAHARASHTRA, KARNATAKA, PUDUCHERRY, TAMIL NAD...

	Order ID	Date	Status	Fulfilment	ship-service-level	Style	Category	Size	ASIN	Courier Status	Qty	Amount	ship-city	ship-state	B2B	RecencyDate
1	0.003865	2022-04-30	5	1	1	845	8	0	5229	0	0	0.072708	764	15	0	2022-04-30
2	0.979281	2022-04-30	3	0	0	531	8	8	666	0	0	0.058918	4412	20	1	2022-04-30
4	0.992046	2022-04-30	3	0	0	752	6	0	4308	0	0	0.102794	1237	31	0	2022-04-30
5	0.979618	2022-04-30	3	0	0	1230	5	8	2904	0	0	0.147564	2054	34	0	2022-04-30
6	0.998214	2022-04-30	3	0	0	180	5	5	1656	0	0	0.116941	1155	5	0	2022-04-30
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
128970	0.989917	2022-05-31	3	0	0	772	8	8	3920	0	0	0.092586	2405	32	0	2022-05-31
128971	0.974602	2022-05-31	3	0	0	1346	5	6	6780	0	0	0.178904	2209	11	0	2022-05-31
128972	0.995608	2022-05-31	3	0	0	236	7	10	4031	0	0	0.123567	2405	32	0	2022-05-31
128973	0.973187	2022-05-31	3	0	0	119	5	9	1300	0	0	0.214721	2257	10	0	2022-05-31
128974	0.998923	2022-05-31	3	0	0	110	5	7	1258	0	0	0.124642	5309	6	0	2022-05-31